Longitudinal neural fingerprinting of opioid-use trajectories

阿片类药物使用轨迹的纵向神经指纹图谱

• 批准号: 10805031
• 负责人: Sarah Yip
• 金额: $ 25.13万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10805031
• 关键词: Abstinence; Address; Award; Basic Science; Behavior Therapy; Behavioral; Big Data; Brain; Buprenorphine; Cessation of life; Clinical; Costs and Benefits; Data; Data Set; Decision Making; Development; Disease; Disease remission; Dropout; Engineering; Epidemic; Fingerprint; Foundations; Functional disorder; Goals; HIV/HCV; Hepatitis C Transmission; Human; Impulsivity; Individual; Individual Differences; International; Intervention; Link; Machine Learning; Magnetic Resonance Imaging; Matched Group; Medicine; Mental disorders; Methadone; Methodology; Network-based; Neurobiology; Neuronal Plasticity; Neurosciences; Opioid; Outcome; Overdose; Overdose reduction; Patients; Pattern; Persons; Pharmaceutical Preparations; Phase; Phenotype; Pilot Projects; Population; Process; Psychiatry; Public Health; Recovery; Relapse; Research; Resources; Risk; Risk Factors; Sample Size; Sampling; Scanning; Therapeutic; Time; Translating; Translations; United States; United States National Academy of Sciences; Work; addiction; brain based; case control; clinical care; clinically relevant; computational neuroscience; criminal behavior; design; illicit opioid; improved; innovation; insight; medication for opioid use disorder; methadone treatment; mortality; neural; neurofeedback; neuroimaging; neuromechanism; novel; novel strategies; opioid epidemic; opioid use; opioid use disorder; overdose risk; patient retention; predict clinical outcome; programs; protective factors; resilience; response; skills; temporal measurement; therapy development; willingness

ABSTRACT Opioid use disorder (OUD) is a significant public health problem in the United States, with overdoses and deaths currently maintaining at epidemic levels. As risk of overdose is highest following relapse and treatment dropout, improved mechanistic understanding of risk and protective factors in individuals currently receiving medications for OUD (MOUD) is urgently needed. To address this, this Cutting-Edge, Basic Science Award (CEBRA) application moves beyond the limitations of traditional case-control designs to rapidly advance our understanding of the neural mechanisms of early MOUD treatment. For decades, clinical neuroimaging has relied on case-control designs in which individuals with a given psychiatric disorder are compared to a group of matched healthy ‘control’ individuals, or a group of individuals distinguished by another individual difference feature. While informative, these approaches by definition focus on group average deviations from a presumed normative population and thus may have relatively limited real world clinical utility. For example, recent findings from machine learning studies of addictions and other disorders indicate that brain networks which distinguish patients from controls are often distinct from brain networks that predict specific clinical outcomes within-group. This striking distinction suggests that person-specific neurobiology is dissociable from group-specific patterns, and thus group-specific findings are unlikely to translate to an improved understanding of person-specific pathophysiology or to treatment. Our innovation, the characterization of neural trajectories during MOUD treatment using dense sampling (i.e., repeated longitudinal assessments of the same individual) will provide unprecedented mechanistic insight into the neurobiological basis of OUD remission. Dense sampling is an emerging methodology that aims to overcome limitations with cross-sectional research that inherently assumes the brain is static and unchanging. This approach is particularly relevant to studying MOUD treatment: MOUD is multiphasic, comprised of medication induction, stabilization, ongoing treatment and eventual discontinuation phases. However, with a few small exceptions, existing neuroimaging efforts are almost exclusively single time-point assessments which, by definition, fail to capture dynamic trajectories of individual risk and resilience that can be used to mechanistically inform treatment advancements. This pilot project therefore applies dense sampling to characterize early trajectories of MOUD recovery at unprecedented temporal resolution—i.e., bi-weekly over three months. To maximize mechanistic insight, complementary clinical and behavioral computational data will also be acquired longitudinally. Borrowing from basic human neuroscience, our dense sampling neuroimaging approach represents a paradigm shift for psychiatry research in general, and holds enormous potential to inform understanding of opioid use disorder remission specifically.

摘要 阿片类药物使用障碍（OUD）是美国的一个重大公共卫生问题，涉及过量和死亡 目前仍处于流行水平。由于药物过量的风险在复发和治疗退出后最高， 对目前正在接受药物治疗的个人的风险和保护因素的机械理解得到提高 对于OUD（MOUD）来说，这是非常必要的。为了解决这个问题，这个尖端基础科学奖（CEBRA） 应用超越了传统病例对照设计的局限性，迅速推进了我们的理解 早期MOUD治疗的神经机制。 几十年来，临床神经影像学一直依赖于病例对照设计，其中具有给定 将精神障碍与一组匹配的健康“对照”个体或一组个体进行比较， 另一个个体差异特征。这些方法虽然信息丰富，但从定义上讲， 对群体平均偏差从一个假定的正常人群，因此可能有相对有限的真实的 世界临床应用。例如，机器学习研究成瘾和其他疾病的最新发现 表明区分患者和对照组的大脑网络通常不同于 预测组内特定的临床结果。这种显著的区别表明， 神经生物学与群体特异性模式是分离的，因此群体特异性发现不太可能转化为 对个人特异性病理生理学的更好理解或治疗。 我们的创新，使用密集采样在MOUD治疗期间表征神经轨迹 (i.e.,对同一个体的重复纵向评估）将提供前所未有的机械洞察力 OUD缓解的神经生物学基础密集抽样是一种新兴的方法，其目的是 克服横截面研究的局限性，因为横截面研究固有地假设大脑是静态和不变的。 这种方法与研究MOUD治疗特别相关：MOUD是多相的，包括 药物诱导、稳定、持续治疗和最终停药阶段。然而，随着一些 除了少数例外，现有的神经影像学工作几乎完全是单一时间点评估， 定义，未能捕捉个人风险和弹性的动态轨迹，可用于机械地 了解治疗进展。因此，该试点项目采用密集采样来表征早期 在前所未有的时间分辨率下的MOUD恢复轨迹-即，三个月内每两周一次。到 最大限度地提高机械的洞察力，补充临床和行为计算数据也将获得 纵向。借用基本的人类神经科学，我们的密集采样神经成像方法 代表了精神病学研究的范式转变，并具有巨大的潜力， 了解阿片类药物使用障碍的缓解。